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A matching procedure for sequential experiments that iteratively learns which covariates improve power
Author(s) -
Kapelner Adam,
Krieger Abba
Publication year - 2023
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13561
Subject(s) - covariate , matching (statistics) , estimator , computer science , randomized experiment , power (physics) , statistics , mathematical optimization , mathematics , machine learning , physics , quantum mechanics
We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials exploiting some subjects' previous assessed responses. Subjects arrive sequentially and are either randomized or paired to a previously randomized subject and administered the alternate treatment. The pairing is made via a dynamic matching criterion that iteratively learns which specific covariates are important to the response. We develop estimators for the average treatment effect as well as an exact test. We illustrate our method's increase in efficiency and power over other allocation procedures in both simulated scenarios and a clinical trial dataset. An R package “ SeqExpMatch ” for use by practitioners is available on CRAN .